SoC-based biomedical embedded system design of arrhythmia detector

Arrhythmia is an irregular heartbeat where the blood may not be delivered effectively throughout the body and cause sudden cardiac arrest (SCA). Immediate treatment is required to prevent SCA. However, most of the existing electrocardiogram (ECG) monitoring devices are bulky, cost expensive and lack arrhythmia detection and classification system. This paper proposes a front-end on-board graphical interface design of System-on-Chip (SoC) based arrhythmia detector which can be used as a first screening device for cardiac disease patient. The system consists of a knowledge-based arrhythmia classifier which is able to identify three types of arrhythmias which are ventricular fibrillation (VF), premature ventricular contractions (PVCs) and second-degree atrioventricular (AV) block. The system has been evaluated and benchmarked with ECG data from MIT-BIH arrhythmia database. The results show that its accuracy is up to 99.25% with a computation time of 6.385 seconds. It is highly portable and relatively inexpensive for installation in small clinics and home monitoring

Congestion diffusion and decongestion strategy in networked traffic

We study the information traffic in Barab\'asi-Albert scale free networks wherein each node has finite queue length to store the packets. It is found that in the case of shortest path routing strategy the networks undergo a first order phase transition i.e., from a free flow state to full congestion sate, with the increasing of the packet generation rate. We also incorporate random effect (namely random selection of a neighbor to deliver packets) as well as a control method (namely the packet-dropping strategy of the congested nodes after some delay time $T$) into the routing protocol to test the traffic capacity of the heterogeneous networks. It is shown that there exists optimal value of $T$ for the networks to achieve the best handling ability, and the presence of appropriate random effect also attributes to the performance of the networks.Comment: 6 pages and 6 figures, all comments are welcom

Radiation-induced melting in coherent X-ray diffractive imaging at the nanoscale

Coherent X-ray diffraction techniques play an increasingly significant role in imaging nanoscale structures which range from metallic and semiconductor samples to biological objects. The conventional knowledge about radiation damage effects caused by ever higher brilliance X-ray sources has to be critically revised while studying nanostructured materials

Sequence variants of interleukin 6 (IL-6) are significantly associated with a decreased risk of late-onset Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Interleukin 6 (IL-6) has been related to beta-amyloid aggregation and the appearance of hyperphosphorylated tau in Alzheimer's disease (AD) brain. However, previous studies relating <it>IL-6 </it>genetic polymorphisms to AD included few and unrepresentative single nucleotide polymorphisms (SNPs) and the results were inconsistent.</p> <p>Methods</p> <p>This is a case-control study. A total of 266 patients with AD, aged≧65, were recruited from three hospitals in Taiwan (2007-2010). Controls (n = 444) were recruited from routine health checkups and volunteers of the hospital during the same period of time. Three common <it>IL-6 </it>haplotype-tagging SNPs were selected to assess the association between <it>IL-6 </it>polymorphisms and the risk of late-onset AD (LOAD).</p> <p>Results</p> <p>Variant carriers of <it>IL-6 </it>rs1800796 and rs1524107 were significantly associated with a reduced risk of LOAD [(GG + GC vs. CC): adjusted odds ratio (AOR) = 0.64 and (CC + CT vs. TT): AOR = 0.60, respectively]. Haplotype CAT was associated with a decreased risk of LOAD (0 and 1 copy vs. 2 copies: AOR = 0.65, 95% CI = 0.44-0.95). These associations remained significant in <it>ApoE e4 </it>non-carriers only. Hypertension significantly modified the association between rs2069837 polymorphisms and the risk of LOAD (<it>p</it>_interaction= 0.03).</p> <p>Conclusions</p> <p><it>IL-6 </it>polymorphisms are associated with reduced risk of LOAD, especially in <it>ApoE e4 </it>non-carriers. This study identified genetic markers for predicting LOAD in <it>ApoE e4 </it>non-carriers.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing